The performance of the D-Wave Systems quantum annealers has increase with each new generation of machine. While the latest machine still can not perform better than the best classical algorithms on multiple CPU/GPU compute hardware, they are narrowing the gap between their annealing QPU and a classical CPU for certain problem types [1]}. It is difficult to compare the performance of quantum annealing and classical heuristics in theory. Predictions of adiabatic quantum computing have some theoretical underpinning, quantum annealing itself does not. Additionally, in empirical comparisons, the quantum annealers are mostly limited by noise and other imperfections in their current state of technology, while classical algorithms and hardware are very mature. Research on improving quantum annealing is ongoing both from a qubit technology perspective and in an operational perspective. A case in point for the latter is exploring tailored annealing schedules different than the nominal linear annealing schedule shown above in Equation REF . For example, inhomogeneous annealing schedules for each qubit can be applied and have shown significant improvements in obtaining higher probabilities for low-energy solutions. [2]}
